arXiv research

New model accounts for scale variation and noise in pairwise comparisons.

Noise-resilient method improves Hurst exponent estimation accuracy in noisy data.

This paper proposes an active metric learning method for clustering with pairwise constraints.

Paper develops active learning for clustering unknown pairwise similarities.

A new method for identifying causal directions in complex systems.

Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.

Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in many application settings matching subsequences (segments) instead of individual samples may bring in additional robustness to noise or local non-causal perturbations. Thi…

We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder t…

Class2Simi reduces noise in noisy label learning by transforming noisy class labels into noisy similarity labels.

Paper introduces efficient top-k selection with differential privacy.

A new noise model for preferential Bayesian optimization using user anchors.

SyncRank recovers global ranking from noisy comparisons with theoretical guarantees.

New algorithm learns permutations mixtures with optimal sample complexity.

With the widespread use of machine learning for classification, it becomes increasingly important to be able to use weaker kinds of supervision for tasks in which it is hard to obtain standard labeled data. One such kind of supervision is provided pairwise---in the form of Similar (S) pairs (if two examples belong to t…

Paper addresses group synchronization with incomplete measurements and proves linear convergence of GPM.

We solve continuous-time latent SDE identifiability using diffusion shifts.

Paper improves privacy in SGD with low noise, achieving optimal risk rates.

The paper addresses monotonicity in machine learning models for fairness and accountability.

The paper improves alignment methods for deep neural networks using geometric and spectral analysis.

Decor protects decentralized learning models from curious users.

GAMI-Net improves neural network interpretability while maintaining accuracy.

Rank regression from pairwise comparisons requires many comparisons to accurately learn model parameters.

We address noisy Euclidean distances in high dimensions, estimating noise levels and correcting distances.

Simple model finds high correlation in retail crypto returns.

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder bu…

We study an economic model where agents trade a variety of products by using one of three competing rules: "need", "greed" and "noise". We find that the optimal strategy for any agent depends on both product composition in the overall market and composition of strategies in the market. In particular, a strategy that do…

Study embeds PC matrices into Grassmannian manifold for geometric interpretation.

Develops new oracle inequalities for Gaussian ranking estimators.

Develops efficient inference for noise heterogeneity in machine learning models.

The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix. However, the pairwise similarity is notoriously known for its vulnerability of noise contamination or the imbalance in samples or features, and thus hinders accurate clustering. To tackl…

Proposes a novel tensor-based approach for multi-level link prediction.

PIN models feature interactions using a neural network that mimics decision trees.

The Johnson-Lindenstrauss Lemma allows for the projection of $n$ points in $p-$dimensional Euclidean space onto a $k-$dimensional Euclidean space, with $k \ge \frac{24\ln \emph{n}}{3ε^2-2ε^3}$, so that the pairwise distances are preserved within a factor of $1\pmε$. Here, working directly with the distributions of the …

Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top $K$ items from noisy pairwise comparisons. In our setting, we are non-actively given $r$ pairwise comparisons between each pair of $n$ items, where each comparison has noi…

We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. W…

Paper proposes a self-supervised method to denoise autoregressive signals with heavy-tailed noise.

Multiplicative noise models are often used instead of additive noise models in cases in which the noise variance depends on the state. Furthermore, when Poisson distributions with relatively small counts are approximated with normal distributions, multiplicative noise approximations are straightforward to implement. Th…

Identifying causal direction in location-scale noise models with hidden variables

Paper tackles noisy comparison oracle for robust clustering algorithms.

Study evaluates how noise affects ANMs' ability to identify causal directions.

This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of $n$ objects can be identified by standard sorting methods using $n log_2 n$ pairwise comparisons. We are interested in natural situations in which relationships among the o…

We examine several aspects of explicability of a classification system built from neural networks. The first aspect is the pairwise explicability, which is the ability to provide the most accurate prediction when the range of possibilities is narrowed to just two. Next we consider explicability in development, which me…

We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Lu…

In this paper we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking,…

This study explores how feature graphs enhance GNNs' performance in modeling interactions.

The functions of proteins and RNAs are determined by a myriad of interactions between their constituent residues, but most quantitative models of how molecular phenotype depends on genotype must approximate this by simple additive effects. While recent models have relaxed this constraint to also account for pairwise in…

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations b…

We consider the problem of PAC learning the most valuable item from a pool of $n$ items using sequential, adaptively chosen plays of subsets of $k$ items, when, upon playing a subset, the learner receives relative feedback sampled according to a general Random Utility Model (RUM) with independent noise perturbations to…